Backend engineer focused on building scalable, reliable, and observable systems.
I value deep understanding over surface-level familiarity. I grow by learning from others’ experience, reading engineering literature, and collaborating on real systems. My interests lie in how distributed systems behave in production and how thoughtful design reduces long-term complexity.
- Start simple, scale when evidence demands it
- Prefer async and event-driven designs for resilience
- Optimize for observability before performance
- Use proven technology unless complexity is justified
- Reliability and clarity beat cleverness
Focused on integrating ML and LLM capabilities into backend systems rather than model research.
- Kafka and RabbitMQ for event-driven workflows and service decoupling
- Redis for caching, rate limiting, and async coordination
- Celery and ARQ for background and scheduled processing
- Kubernetes for container orchestration and service isolation
- Reading engineering books and postmortems
- Studying architectures of large-scale platforms
- Learning through collaboration, reviews, and real systems
Currently reading: Designing Data-Intensive Applications
Building reliable, observable, cloud-native backend systems on Kubernetes.
- Backend and platform collaboration
- Distributed systems discussions
- Open-source contributions
Learning continuously. Building thoughtfully. Growing through collaboration.